import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from model import *

# 定义训练的设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # 注释同MLP模型

# 准备数据集
train_data = torchvision.datasets.CIFAR10("../CIFAR10_dataset", train=True, transform=torchvision.transforms.ToTensor(),
                                          download=True)
test_data = torchvision.datasets.CIFAR10("../CIFAR10_dataset", train=False, transform=torchvision.transforms.ToTensor(),
                                         download=True)
# length长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集的长度为：{}".format(train_data_size))
print("测试数据集的长度为：{}".format(test_data_size))

# 利用DataLoader来加载数据集
train_dataloader = DataLoader(train_data, batch_size=64, shuffle=True)
test_dataloader = DataLoader(test_data, batch_size=64, shuffle=True)

# 搭建神经网络
model = RESNET()
model = model.to(device)

# 损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn = loss_fn.to(device)

# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(model.parameters(), learning_rate)

# 设置训练网络的一些参数

# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 100

# 添加tensorboard
Writer = SummaryWriter("logs_RESNET16_CIFAR10")
for i in range(epoch):
    print("---------第{}轮训练开始---------".format(i + 1))
    # 训练集损失
    total_loss_step = 0
    # 训练步骤开始
    model.train()
    for data in train_dataloader:
        imgs, targets = data
        imgs = imgs.to(device)
        targets = targets.to(device)
        outputs = model(imgs)
        loss = loss_fn(outputs, targets)

        # 优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_train_step = total_train_step + 1
        if total_train_step % 100 == 0:
            print("训练次数：{}，Loss：{}".format(total_train_step, loss.item()))
            Writer.add_scalar("train_loss", loss.item(), total_train_step)

    # 测试步骤开始
    model.eval()
    total_test_loss = 0
    total_accuracy = 0
    with torch.no_grad():
        for data in test_dataloader:
            imgs, targets = data
            imgs = imgs.to(device)
            targets = targets.to(device)
            outputs = model(imgs)
            loss = loss_fn(outputs, targets)
            total_test_loss = total_test_loss + loss.item()
            accuracy = (outputs.argmax(1) == targets).sum()
            total_accuracy = total_accuracy + accuracy.item()

    print("整体测试集上的Loss:{}".format(total_test_loss))
    print("整体测试集上的正确率：{}".format(total_accuracy / test_data_size))
    total_test_step = total_test_step + 1
    Writer.add_scalar("test_loss", total_test_loss, total_test_step)
    Writer.add_scalar("test_accuracy", total_accuracy / test_data_size, total_test_step)

    torch.save(model, "resnet18_{}.pth".format(i))
    print("模型已保存")

Writer.close()
